Ligand co-crystallization of aminoacyl-tRNA synthetases from infectious disease organisms
Bibliographic record
Abstract
Aminoacyl-tRNA synthetases (aaRSs) charge tRNAs with their cognate amino acid, an essential precursor step to loading of charged tRNAs onto the ribosome and addition of the amino acid to the growing polypeptide chain during protein synthesis. Because of this important biological function, aminoacyl-tRNA synthetases have been the focus of anti-infective drug development efforts and two aaRS inhibitors have been approved as drugs. Several researchers in the scientific community requested aminoacyl-tRNA synthetases to be targeted in the Seattle Structural Genomics Center for Infectious Disease (SSGCID) structure determination pipeline. Here we investigate thirty-one aminoacyl-tRNA synthetases from infectious disease organisms by co-crystallization in the presence of their cognate amino acid, ATP, and/or inhibitors. Crystal structures were determined for a CysRS from Borrelia burgdorferi bound to AMP, GluRS from Borrelia burgdorferi and Burkholderia thailandensis bound to glutamic acid, a TrpRS from the eukaryotic pathogen Encephalitozoon cuniculi bound to tryptophan, a HisRS from Burkholderia thailandensis bound to histidine, and a LysRS from Burkholderia thailandensis bound to lysine. Thus, the presence of ligands may promote aaRS crystallization and structure determination. Comparison with homologous structures shows conformational flexibility that appears to be a recurring theme with this enzyme class.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".